In the current state of technology, when a user types an uncommon word such as “Alamo” into a query for a search engine or a relational database, then the engine may return a list of irrelevant information as “relevant results” to your query. Relational databases exist where a user can specify a reference document and find relevant related documents to that reference document. A few relational databases exist where a particular field may be pre-designated by the author of the reference document to assist in narrowing a user's query to find relevant related material regarding the reference document. The pre-designated field typically summarizes the main ideas conveyed by the reference document as determined by the author of the reference document. The user of the relational database may choose to use this pre-designated field as the content of the query. However, these relational databases typically return a list of related documents based upon some form of exact word matching.
The prior art technologies may generally lack the ability to allow the user to more narrowly target the desired related documents. The prior art technologies may generally lack the ability to allow the user to more narrowly target a specific aspect of interest in the reference document. The prior art technologies may generally lack the convenience of having an automated link to those related documents. The prior art technologies may generally lack the ability to return a list of related documents that convey a semantically similar idea but use literally different words to convey that idea.
Extensible markup language (XML) is becoming an increasingly popular method of labeling and tagging digital material containing information. Like most tagging schemas, XML suffers from a number of limitations. One limitation of XML is the manual process employed to choose and apply the tags. Not only are tags often chosen manually, which may be a costly process, but also XML has no built in understanding of concepts that are similar to one another. In XML, for example, the tag “automobile” and the tag “car” are wholly unrelated items. Typically, this presents considerable problems, because information from different sources that has been structured using different tagging schema cannot without human intervention be reconciled, even when important conceptual similarities exist. This lack of conceptual understanding may be a considerable handicap to the success of XML becoming the de facto standard for information exchange.
As noted above, an XML document contains a particular schema and tag set. XML tags are defined in an XML schema, which defines the content type as well as the name. The human-readable XML tags provide a simple data format. A particular XML tag may be paired with a value to be a tag-value pair. For example, the tag “vehicle” may be paired with the value “car” to become the tag-value pair of “Vehicle=Car.” The XML tag structure defines what the elements contain in the XML document. Unlike, HTML which uses predefined tags, XML allows tags to be defined by the developer of the document.
Thus, numerous variables may be put into the tag fields in different schemas. For example in a second XML schema, a user may use the tag-value pair of “Product=Automobile.” Each tag-value pair will have a descriptive field filled with unstructured content. For example, “1967 Ford Mustang with two doors, rear wheel drive, and V-8, 5.0 liter engine.”
XML tags also fail to highlight the relationships between subjects. Termed “idea distancing”, there are often vital relationships between seemingly separately tagged subjects such as for example, “/wing design/low drag/” compared to “/aerofoil/efficiency/”. The first category may contain information about the way the wings are designed to achieve low air resistance. The latter category discusses ways in which efficient aerofoils are made. Obviously, there will be a degree of overlap between these categories and because of this, a user may be interested in the contents of both. However, without understanding the meanings of the category names, there is no clear correlation between the two.
Further complications arise when a topic incorporates multiple themes. Should an article about ‘technology development in Russia within the context of changing foreign policy’ be classified as (i) Russian technology (ii) Russian foreign policy, or (iii) Russian economics? The decision process is both complex and time consuming and introduces yet more inconsistency, particularly when the sheer number of options available to a user is considered. For example, over 800 tags for general newspaper subjects make the task of choosing a potentially basic subject description, in a reasonable timescale, an even more challenging process.
These limitations occur because XML is not a set of standard tag definitions, but XML is a set of definitions that allow individual to define tags. This means that if two organizations are going to interoperate and utilize the same meaning for the same tags, they have to explicitly agree their definitions in advance. The organizations will need to establish a fixed set of field names on each document. The organizations will need to have the entire XML document adhere to that schema. To reconcile these limitation the above tasks, manual tagging, linking, or categorizing of the raw data must be performed prior to perform information operations on the collective raw data.
Related technology, such as a relational database, performs information operations on structured information. However, individuals must manually map the relationships and links of unstructured information in the relational database.
As noted above, XML may assist in facilitating information operations on semi-structured and unstructured information.
In general, structured information may include information possessing structure elements of a data record designed to assist in processing that information by some form of rule-based system that expects specific sets of input values and specific types of fields. Structured information generally lacks contextual text or audio speech information, and consists of fielded/tagged information that adheres to a predefined schema or taxonomy. A typical example of structured information is a transaction record for a bank. The transaction record contains fields that have precisely defined allowable values, such as ‘name’, ‘account number’, ‘amount’, ‘date’, etc., but lacks free-form contextual fields.
Semi-structured information refers to a hybrid system, typified in XML systems. Accordingly, a ‘data record’ consists of some fields that are compulsory and have definable values, typically from some definable set, and also consists of fields that contain ‘free text’ or information that is not part of a definable set. Semi structure information may contain textual or audio speech information that contains some defined structured, such as meta tags, relating to the conceptual content of the information. The structure of the information may or may not include tags/meta information that augment the content of the information but usually do not explain or relate to the context of the information. Semi-structured information typically has limited fielded information/meta tagging information relating to how to process the data or where to store/retrieve this information in some taxonomy/indexing system. For example, an XML record has an inherent position within the overall XML data document in which the record resides within.
A typical example of semi-structured information is a news web page that contains a story, title and maybe some category tags that follow a predetermined taxonomy/schema and relate to the content of the document tagged with the information. The tags may contain date information, author information and news provider information, but these meta tags/fields/structured elements do not relate to the context of the content.
Another typical example of semi-structured information is an XML document or XML ‘record’ that is part of a larger XML data set. The XML record has a definable ‘position’ within the larger piece of XML. For example, the ACT is structured by defining it as a sub element of a PLAY and giving the ACT a number which enables some information processing to happen based on this structure.
<SHAKESPEARE>                <PLAY>                    <ACT>                            <NUMBER>1</NUMBER>                <SPEECH>...free text...</SPEECH>                                    </ACT>            <ACT>                            <NUMBER>2</NUMBER>                <SPEECH>...free text...</SPEECH>                                    </ACT>                        </PLAY>        
</SHAKESPEARE>
Unstructured information lacks definable/reliable in/out fields that assist in processing that information, by some form of rule-based system that expects specific sets of input values and specific types of fields. Unstructured information may contain a piece of textual or audio speech information that lacks any defined structured meta tags relating to the conceptual content of the information. Referring to the above example, the actual words in the SPEECH by themselves could be considered unstructured information.